Among the many mysteries in medical science, it is known that minority and low-income patients experience greater pain than other parts of the population. This is true regardless of the root cause of the pain and even when comparing patients with similar levels of disease severity. Now, a team of researchers, including Stanford computer scientist Jure Leskovec, has used AI to more accurately and more fairly measure severe knee pain.
A Definitive Answer
“By using X-rays exclusively, we show the pain is, in fact, in the knee, not somewhere else,” Leskovec says. “What’s more, X-rays contain these patterns loud and clear but KLG cannot read them. We developed an AI-based solution that can learn to read these previously unknown patterns.”
Factoring All Pain Points
Leskovec and his collaborators began with a diverse database of over 4,000 patients and more than 35,000 images of their damaged knees. It included almost 20 percent Black patients and large numbers of lower-income and lower-educated patients.
The machine learning algorithm then evaluated the scans of all the patients and other demographic and health data, such as race, income, and body mass index, and predicted patient pain levels. The team was able to then parse the data in various ways, separating just the Black patients, for instance, or looking only at low-income populations, to compare algorithmic performance and test various hypotheses.
The bottom line, Leskovec says, is that the models trained using the diverse training data sets were the most accurate in predicting pain and reduced the racial and socioeconomic disparity in pain scores.
“The pain is in the knee,” Leskovec says. “Still useful as it is, KLG was developed in the 1950s using a not very diverse population and, consequently, it overlooks important knee pain indicators. This shows the importance to AI of using diverse and representative data.”
Better Clinical Decision Making
Leskovec notes that AI will certainly not replace the physician’s expertise in pain management decisions; rather, he sees it aiding decisions. The algorithm not only scores pain more accurately but presents additional visual data that could prove helpful in the clinic such as “heat maps” of areas of the knee most affected by pain that might help physicians notice problems not apparent in the KLG evaluation and, for instance, choose to prescribe fewer opioids and get knee replacements to more patients in these underserved populations.
As Leskovec’s work shows, artificial intelligence balances inequalities. It more accurately reads knee pain and could greatly expand and improve treatment options for these traditionally underserved patients.
“We think AI could become a powerful tool in the treatment of pain across all parts of society,” Leskovec says.
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